It allows us to directly minimize the loss over the generated map for accurate localization maps with a similar confidence for all object parts. Given a set of tokens from a pretrained SST model [1]}, the associated maps are first binarized to allow us extracting bounding boxes over potential ROIs. These are considered proposals candidates that potentially cover object regions with varying certainty. To measure certainty, we exploit the response of a pretrained CNN classifier to indicate most relevant ROI. To avoid overfitting to a single proposal, we consider top-\(P\)  confident ROI proposals, and randomly select ones of them for sampling. This final proposal is used to constrain sampling of pixel-wise pseudo-labels where foreground pixels are sampled inside the bounding box of the proposals. Sampling is guided by the activation magnitude, where strong activations are favored to be foreground. Background pixels are sampled outside the bounding box by favoring low activations. Sampling of pseudo-labels is done randomly at each Stochastic Gradient Descent (SGD) iteration, which has been shown to be more effective and robust against overfitting than static and noisy pseudo-labels [2]}, [3]}. Overall, this sampling allows for the emergence of accurate localization maps, from few pixels toward segments and the entire object. This can be seen as a fill-in-the-gap game where only random pixels are selected as foreground/background and the localization network has to generalize to similar regions. Then, through iterations, this random sampling is expected to stimulate convolution filters to have similar response to visually similar nearby pixels.
